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The emerging vehicular communication systems have the promise to greatly improve road safety, traffic efficiency, and driver convenience. However, many envisioned applications require a user to constantly reveal his locations, the user is in danger of loosing his location privacy. Hence, in the context of vehicular communication systems, a meaningful location privacy metric is indispensable for the development of cost-effective location privacy-protection mechanisms.
Existing privacy metrics have various limitations which make them incapable to fully capture a user´s location privacy and give meaningful and accurate privacy measurements. In this dissertation, we aim at developing a location privacy metric for the users of vehicular communication systems. Based on a capture-model-measure paradigm, the metric captures related information in snapshots, processes the information, and gives quantitative measurements of a user´s level of location privacy in the system. To truthfully reflect the underlying privacy values, we extend the metric and provide solutions for measuring a user´s location privacy in multiple dimensions such as privacy in timely-ordered snapshots and privacy in snapshots with interrelated users. We evaluate our approach by extensive simulations as well as by a proof-of-concept implementation based on realistic dataset.
As a result, we give a comprehensive answer to how to measure a user´s location privacy in vehicular communication systems while taking into account domain specific aspects and privacy in multiple dimensions. The location privacy metric fills an important gap in current research. Furthermore, our measurement approach provides insights into the cause of the location privacy problem, on which cost-effective privacy-protection mechanisms can be developed to benefit the users of vehicular communication systems.